Best practice data life cycle approaches for the life sciences
- Griffin, Philippa, Khadake, Jyoti, LeMay, Kate, Lewis, Suzanna, Orchard, Sandra, Pask, Andrew, Pope, Bernard, Roessner, Ute, Russell, Keith, Seemann, Torsten, Treloar, Andrew, Tyagi, Sonika, Christiansen, Jeffrey, Dayalan, Saravanan, Gladman, Simon, Hangartner, Sandra, Hayden, Helen, Ho, William, Keeble-Gagnère, Gabriel, Korhonen, Pasi, Neish, Peter, Prestes, Priscilla, Richardson, Mark, Watson-Haigh, Nathan, Wyres, Kelly, Young, Neil, Schneider, Maria
- Authors: Griffin, Philippa , Khadake, Jyoti , LeMay, Kate , Lewis, Suzanna , Orchard, Sandra , Pask, Andrew , Pope, Bernard , Roessner, Ute , Russell, Keith , Seemann, Torsten , Treloar, Andrew , Tyagi, Sonika , Christiansen, Jeffrey , Dayalan, Saravanan , Gladman, Simon , Hangartner, Sandra , Hayden, Helen , Ho, William , Keeble-Gagnère, Gabriel , Korhonen, Pasi , Neish, Peter , Prestes, Priscilla , Richardson, Mark , Watson-Haigh, Nathan , Wyres, Kelly , Young, Neil , Schneider, Maria
- Date: 2018
- Type: Text , Journal article
- Relation: F1000 Research Vol. 6, no. (2018), p. 1-28
- Full Text:
- Reviewed:
- Description: Throughout history, the life sciences have been revolutionised by technological advances; in our era this is manifested by advances in instrumentation for data generation, and consequently researchers now routinely handle large amounts of heterogeneous data in digital formats. The simultaneous transitions towards biology as a data science and towards a 'life cycle' view of research data pose new challenges. Researchers face a bewildering landscape of data management requirements, recommendations and regulations, without necessarily being able to access data management training or possessing a clear understanding of practical approaches that can assist in data management in their particular research domain. Here we provide an overview of best practice data life cycle approaches for researchers in the life sciences/bioinformatics space with a particular focus on 'omics' datasets and computer-based data processing and analysis. We discuss the different stages of the data life cycle and provide practical suggestions for useful tools and resources to improve data management practices. © 2018 Griffin PC et al.
- Authors: Griffin, Philippa , Khadake, Jyoti , LeMay, Kate , Lewis, Suzanna , Orchard, Sandra , Pask, Andrew , Pope, Bernard , Roessner, Ute , Russell, Keith , Seemann, Torsten , Treloar, Andrew , Tyagi, Sonika , Christiansen, Jeffrey , Dayalan, Saravanan , Gladman, Simon , Hangartner, Sandra , Hayden, Helen , Ho, William , Keeble-Gagnère, Gabriel , Korhonen, Pasi , Neish, Peter , Prestes, Priscilla , Richardson, Mark , Watson-Haigh, Nathan , Wyres, Kelly , Young, Neil , Schneider, Maria
- Date: 2018
- Type: Text , Journal article
- Relation: F1000 Research Vol. 6, no. (2018), p. 1-28
- Full Text:
- Reviewed:
- Description: Throughout history, the life sciences have been revolutionised by technological advances; in our era this is manifested by advances in instrumentation for data generation, and consequently researchers now routinely handle large amounts of heterogeneous data in digital formats. The simultaneous transitions towards biology as a data science and towards a 'life cycle' view of research data pose new challenges. Researchers face a bewildering landscape of data management requirements, recommendations and regulations, without necessarily being able to access data management training or possessing a clear understanding of practical approaches that can assist in data management in their particular research domain. Here we provide an overview of best practice data life cycle approaches for researchers in the life sciences/bioinformatics space with a particular focus on 'omics' datasets and computer-based data processing and analysis. We discuss the different stages of the data life cycle and provide practical suggestions for useful tools and resources to improve data management practices. © 2018 Griffin PC et al.
Human blood MAIT cell subsets defined using MR1 tetramers
- Gherardin, Nicholas, Souter, Michael, Koay, Hui-Fern, Mangas, Kirstie, Seemann, Torsten, Stinear, Timothy, Eckle, Sidonia, Berzins, Stuart, d'Udekem, Yves, Konstantinov, Igor, Fairlie, David, Ritchie, David, Neeson, Paul, Pellicci, Daniel, Uldrich, Adam, McCluskey, James, Godfrey, Dale
- Authors: Gherardin, Nicholas , Souter, Michael , Koay, Hui-Fern , Mangas, Kirstie , Seemann, Torsten , Stinear, Timothy , Eckle, Sidonia , Berzins, Stuart , d'Udekem, Yves , Konstantinov, Igor , Fairlie, David , Ritchie, David , Neeson, Paul , Pellicci, Daniel , Uldrich, Adam , McCluskey, James , Godfrey, Dale
- Date: 2018
- Type: Text , Journal article
- Relation: Immunology and Cell Biology Vol. 96, no. 5 (2018), p. 507-525
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- Description: Mucosal-associated invariant T (MAIT) cells represent up to 10% of circulating human T cells. They are usually defined using combinations of non-lineage-specific (surrogate) markers such as anti-TRAV1-2, CD161, IL-18R
- Authors: Gherardin, Nicholas , Souter, Michael , Koay, Hui-Fern , Mangas, Kirstie , Seemann, Torsten , Stinear, Timothy , Eckle, Sidonia , Berzins, Stuart , d'Udekem, Yves , Konstantinov, Igor , Fairlie, David , Ritchie, David , Neeson, Paul , Pellicci, Daniel , Uldrich, Adam , McCluskey, James , Godfrey, Dale
- Date: 2018
- Type: Text , Journal article
- Relation: Immunology and Cell Biology Vol. 96, no. 5 (2018), p. 507-525
- Full Text:
- Reviewed:
- Description: Mucosal-associated invariant T (MAIT) cells represent up to 10% of circulating human T cells. They are usually defined using combinations of non-lineage-specific (surrogate) markers such as anti-TRAV1-2, CD161, IL-18R
Wave 2 strains of atypical Vibrio cholerae El Tor caused the 2009-2011 cholera outbreak in Papua New Guinea
- Greenhill, Andrew, Mutreja, Ankur, Bulach, Dieter, Belousoff, Matthew, Jonduo, Marinjho, Collins, Deirdre, Kas, Monalisa, Wapling, Johanna, Seemann, Torsten, Lafana, Alice, Dougan, Gordon, Brown, Mark, Horwood, Paul
- Authors: Greenhill, Andrew , Mutreja, Ankur , Bulach, Dieter , Belousoff, Matthew , Jonduo, Marinjho , Collins, Deirdre , Kas, Monalisa , Wapling, Johanna , Seemann, Torsten , Lafana, Alice , Dougan, Gordon , Brown, Mark , Horwood, Paul
- Date: 2019
- Type: Text , Journal article
- Relation: Microbial genomics Vol. 5, no. 3 (2019), p. 1-5
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- Reviewed:
- Description: Vibrio cholerae is the causative agent of cholera, a globally important human disease for at least 200 years. In 2009-2011, the first recorded cholera outbreak in Papua New Guinea (PNG) occurred. We conducted genetic and phenotypic characterization of 21 isolates of V. cholerae, with whole-genome sequencing conducted on 2 representative isolates. The PNG outbreak was caused by an atypical El Tor strain harbouring a tandem repeat of the CTX prophage on chromosome II. Whole-genome sequence data, prophage structural analysis and the absence of the SXT integrative conjugative element was indicative that the PNG isolates were most closely related to strains previously isolated in South-East and East Asia with affiliations to global wave 2 strains. This finding suggests that the cholera outbreak in PNG was caused by an exotic (non-endemic) strain of V. cholerae that originated in South-East Asia.
- Authors: Greenhill, Andrew , Mutreja, Ankur , Bulach, Dieter , Belousoff, Matthew , Jonduo, Marinjho , Collins, Deirdre , Kas, Monalisa , Wapling, Johanna , Seemann, Torsten , Lafana, Alice , Dougan, Gordon , Brown, Mark , Horwood, Paul
- Date: 2019
- Type: Text , Journal article
- Relation: Microbial genomics Vol. 5, no. 3 (2019), p. 1-5
- Full Text:
- Reviewed:
- Description: Vibrio cholerae is the causative agent of cholera, a globally important human disease for at least 200 years. In 2009-2011, the first recorded cholera outbreak in Papua New Guinea (PNG) occurred. We conducted genetic and phenotypic characterization of 21 isolates of V. cholerae, with whole-genome sequencing conducted on 2 representative isolates. The PNG outbreak was caused by an atypical El Tor strain harbouring a tandem repeat of the CTX prophage on chromosome II. Whole-genome sequence data, prophage structural analysis and the absence of the SXT integrative conjugative element was indicative that the PNG isolates were most closely related to strains previously isolated in South-East and East Asia with affiliations to global wave 2 strains. This finding suggests that the cholera outbreak in PNG was caused by an exotic (non-endemic) strain of V. cholerae that originated in South-East Asia.
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